Towards Computational Provenance: Carrying Causal-State Evidence in Generated Text
Researchers are investigating computational provenance to determine if LLM outputs can carry detectable evidence of their internal causal states.
- Current LLM outputs lack inherent evidence of their internal computational paths.
- Computational provenance aims to make internal model states detectable through text.
- Testing was conducted on both modular feed-forward networks and transformer models.
- The method focuses on distinguishing between different internal pathways that lead to identical outputs.
A new study explores the concept of computational provenance, which addresses the inability of standard LLM outputs to provide verifiable evidence of their internal processing. Currently, an AI's response does not inherently reveal which specific internal neurons or pathways were activated to reach a conclusion.
The researchers tested this concept using two different architectures: a modular feed-forward neural network and a transformer-based model. Both were trained on arithmetic tasks designed with multiple possible internal pathways to reach the same result.
By attempting to embed detectable evidence of these internal states into the generated text, the study seeks to create a method for verifying the logic and causal history behind an AI's response.
Provides a potential framework for debugging and verifying model logic.
Represents a novel intersection of interpretability and information theory.
Could lead to more transparent and verifiable AI decision-making processes.
- Computational Provenance
- The ability to trace or detect the specific internal computational steps that led to a specific output.
- Causal-State
- The specific internal configuration of a model that directly influences the resulting output.
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